Definition of Tumor Volume Based on 18F-Fludeoxyglucose Positron Emission Tomography in Radiation Therapy for Liver Metastases: An Relational Analysis Study between Image Parameters and Image Segmentation Methods

Abstract

The surgical resection was occurred mainly in liver metastasis before the development of radiation therapy techniques. Recently, Radiation therapy is increased gradually due to the development of radiation dose delivery techniques. 18F-FDG PET image showed better sensitivity and specificity in liver metastasis detection. This image modality is important in the radiation treatment with planning CT for tumor delineation. In this study, we applied automatic image segmentation methods on PET image of liver metastasis and examined the impact of image factors on these methods. We selected the patients who were received the radiation therapy and 18F-FDG PET/CT in Korea Cancer Center Hospital from 2009 to 2012. Then, three kinds of image segmentation methods had been applied; The relative threshold method, the Gradient method and the region growing method. Based on these results, we performed statistical analysis in two directions. 1. comparison of GTV and image segmentation results. 2. performance of regression analysis for relation between image factor affecting image segmentation techniques. The mean volume of GTV was 60.9±65.9 cc and the GTV40% was 22.43±35.27 cc, and the GTV50% was 10.11±17.92 cc, the GTVRG was 32.89±36.84 cc, the GTVGD was 30.34±35.77 cc, respectively. The most similar segmentation method with the GTV result was the region growing method. For the quantitative analysis of the image factors which influenced on the region growing method, we used the standardized coefficient β, factors affecting the region growing method show GTV, TumorSUVMAX/MIN, SUVmax, TBR in order. The result of the region growing (automatic segmentation) method showed the most similar result with the CT based GTV and the region growing method was affected by image factors. If we define the tumor volume by the auto image segmentation method which reflect the PET image parameters, more accurate and consistent tumor contouring can be done. And we can irradiate the optimized radiation dose to the cancer, ultimately.